Data Processing

In-memory data processing to transform data as it arrives; perform data filtering, data blending and data enrichment at scale, to prepare for analytics and machine learning jobs

Data Cleansing

Minimize data preparation time using various, transformation operators like filtering, imputation and more.

Data Blending

Combine multiple data streams or batch sources into a single stream or table.

Data Blending

Combine multiple data streams or batch sources into a single stream or table.

Data Enrichment

Enhance data with external sources, reference tables and master data repositories, using various lookups, web-services and expressions.

Statistical and Temporal Analytics

In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.

Statistical and Temporal Analytics

In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.

Custom Processing

Use various native Apache Spark and Apache Storm-based operators, and languages including Java, Scala, SQL, Python, for hand-coding any custom logic.

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StreamAnalytix Lite

StreamAnalytix Lite makes Spark easy and expands the base of users that can build Spark based applications. It off¬ers an easy-to-use visual Integrated Development Environment (IDE) to build, deploy and manage Spark based enterprise grade applications.

Operationalize Machine Learning at Scale with StreamAnalytix

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blog Sep 02, 2019

Detect and prevent insider threats with real-time data processing and machine learning

Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent.

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